The autonomic nervous system regulates involuntary functions of nearly all parts of the body in health and disease. It is comprised of two main subdivisions known as the sympathetic nervous system (sympathetic system) and the parasympathetic nervous system (parasympathetic system). The sympathetic system is the system that helps the body respond to stressful situations, and is often referred to as the “fight or flight” system. For example, under stressful conditions the sympathetic system increases the rate in which neurons are fired in order to increase the heart rate, elevate blood pressure, and slow down the digestive process. In contrast, the parasympathetic system helps the body preserve and restore energy. It is often referred to as the “rest and digest” system. For example, when one relaxes by resting in a chair, the parasympathetic system slows the heart, lowers blood pressure, and speeds the digestive process.
Under normal resting or sleeping conditions, the parasympathetic system is dominant. The sympathetic system is normally activated with the addition of external stressful conditions. However, certain conditions such as chronic stress, disease, and emotion, can alter the natural balance between the parasympathetic system and the sympathetic system. These factors generally create a persistent elevation in activity in the sympathetic system and a reduction in activity in the parasympathetic system or vise versa. If not controlled, such an imbalance in the autonomous nervous system can impair the functioning of many organs including the heart, vasculature, gastrointestinal (GI) track, kidneys, and lungs. Such impairment can lead to conditions such as altered blood pressure, heart disease, vascular disease, GI track immobility, kidney failure, and other organ related conditions.
Today, medications are available that affect the autonomic nervous system, such as ACE-inhibitors, beta-blockers, and anti-depressants. These medicines are used to treat altered blood pressure, irregular heart rhythm, chronic fatigue, diabetes, depression, and other conditions related to the autonomic nervous system. These medicines affect the synthesis, release, uptake, and re-uptake of the body's neural chemistry by acting on the receptors in neurons or muscles located in the various areas of the body, such as the brain, heart, kidney, and blood vessels. Many patients use several of these medications simultaneously; thus, it is increasingly important to be able to measure the response of the autonomic nervous system to ensure that the medications are having the desired effects and that a combination of medications is not creating an undesirable imbalance in the autonomic system.
Injury and disease can also have an affect on the autonomic nervous system. For example, diabetes often leads to a condition known as Diabetic Autonomic Neuropathy, which is a condition whereby there is damage to the autonomic nerves. This, in turn, can lead to poor peripheral blood flow, GI track immobility, sexual dysfunction, kidney disease, blindness and silent myocardial ischemia. Silent myocardial ischemia is a condition whereby the patient experiences episodes of blood flow constriction to the heart muscle that is often unnoticed because of an absence of chest pain due to a concurrent loss of sensory neurons. Conditions such as these require that the autonomic nervous system be closely and accurately monitored.
An effective method to monitor the autonomic nervous system is to monitor the function of the heart and the lungs and use the information gathered to derive information regarding the autonomic nervous system. In other words, the heart can be used as a “window” through which it is possible to study the activity of the autonomic nervous system. Heart rate is equal to the number of heartbeats occurring within a specific length of time, and is normally measured in beats per minute (bpm). For example, heart rates above 100 bpm (known as tachycardia) are generally considered to result from activity in the sympathetic system, while heart rates below 60 bpm (known as bradycardia), are generally considered to result from the activity in the parasympathetic system.
However, because the heart rate is influenced over time by both the sympathetic and parasympathetic systems, the average or mean heart rate is not the optimum indicator for monitoring the state of balance within the autonomic nervous system. A better picture can be derived using the instantaneous heart rate. The instantaneous heart rate can be determined by measuring the time interval between two heartbeats using a standard electrocardiogram (EKG). An accelerating heart rate will exhibit a decreasing time interval between beats, while a decelerating heart rate will exhibit an increasing time interval between beats. By measuring spontaneous changes in heart rate, the autonomic nervous system can be monitored more accurately. The parasympathetic system can cause a very fast response, capable of being observed on the next heartbeat (1 to 3 seconds), while response to sympathetic system activity is typically slower, often taking more than five heart beats (10 to 20 seconds). This makes it possible to distinguish activity within the two systems by observing the characteristics of the heart rhythm using frequency-domain analysis, which is well known in the art.
Frequency-domain analysis is a type of spectral analysis typically performed using mathematical modeling methods such as Fast Fourier Transforms (FFT) or autoregressive (AR) techniques. These techniques are used to study the frequency content of the instantaneous heart rate. In applying these techniques, a data sample is obtained over a five minute period (for short term studies) or a 24 hour period (for long-term studies). FFT and AR techniques can be used to process the data sample to separate the slow responding sympathetic activities from the quicker responding parasympathetic activities. However, because these frequency domain techniques do not provide for a means to locate the time events occurring within a data sample, they are most useful for studying short term steady state conditions, meaning situations where the data is consistent across the sample time. For short term studies, this requires the patient to remain motionless during the time period (typically five minutes) in which the data is being gathered. Patient movement, including small movements such as coughing and talking, can cause the accuracy of the information gathered to decrease.
In order to compensate for this shortcoming in pure frequency domain analysis, techniques have been used to modify the FFT and AR techniques to approximate a time domain analysis in addition to a frequency domain analysis. A short term FFT can be performed on smaller blocks of data from within the data sample, as opposed to using the entire data sample. This technique assumes that the data is quasi-stationary, and uses a sliding window within the data sample for choosing the data to analyze. This introduces a time dependent factor or time dependent localization into the analysis. However, this technique results in a trade-off between frequency domain analysis and time domain analysis. Choosing shorter windows within the data results in poorer frequency resolution, while increasing the window length decreases the time domain resolution. This shortcoming can create inaccuracies in the analysis of many types of biological data.
To address these inaccuracies, newly developed advanced mathematical techniques have been employed, such as the Wigner distribution and the Cohen class of time frequency distributions. However, these processes are quadratic in nature; thus, they produce undesirable cross-terms and interferences. This makes their usefulness in analyzing biological data limited. More recently, the technique of wavelet transformation has been considered as a means for processing heart rate data. Wavelet transformation is a mathematical technique known in the art. The technique is effective for analyzing transient variations within a time series, and thus appears to be well suited for spectral analysis of non-stationary signals such as those found in biological data. However, the complexity of wavelet transformation techniques has made real-time implementation difficult prior to the present invention.
What is needed is an effective, non-invasive method of analyzing biological data including the instantaneous heart rate and the respiratory activity to provided accurate, meaningful autonomic nervous system assessment from real-time heart rate variability data. Additionally, it is desirable to shorten the analysis period and improve the resolution of the processed results. This information can then be used to monitor the autonomic nervous system more accurately than previously possible, and further assist medical personnel in the diagnosis and treatment of related conditions.